Error Mitigation Optuna (IBM)

import sys
import os
sys.path.insert(0, os.path.abspath('../'))
sys.path.insert(0, os.path.abspath('../../'))

import qiskit
provider = qiskit.IBMQ.load_account()

import optuna
from qiskit import Aer
from qiskit.utils import QuantumInstance
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter
from qiskit_optimization.algorithms import MinimumEigenOptimizer
from qiskit.algorithms import QAOA

from shared.QiskitMaxcut import *
from ibm.ibm_parameters import *

%matplotlib inline

print_parameters()

Optimizers: ['SPSA', 'QN-SPSA', 'COBYLA'] with MaxIter of 5
Number of shots: 8000
Repetitions: [ 1; 5 ]
Gamma value interval: [ -3.141592653589793; 3.141592653589793 ]
Beta value interval: [ 3.141592653589793;-3.141592653589793 ]
Number of Optuna Trials: 5
Noise Backend Name: ibmq_toronto

# ---- Define graph and MaxCut ----
graph = load_graph()
max_cut = Maxcut(graph)
max_cut_qubo = max_cut.to_qubo()
max_cut.draw()

def init_qaoa(optimizer, reps, init_parameters):
    quantum_instance = QuantumInstance(
                    backend=Aer.get_backend(DEFAULT_QASM_SIMULATOR),
                    shots=SHOTS,
                    measurement_error_mitigation_cls=CompleteMeasFitter,
                    measurement_error_mitigation_shots=SHOTS)

    if optimizer == "QN-SPSA":
        # generate QAOA to get ansatz
        pre_qaoa = QAOA(optimizer=COBYLA(maxiter=1, tol=0), quantum_instance=quantum_instance, reps=reps, initial_point=init_parameters)
        MinimumEigenOptimizer(pre_qaoa).solve(max_cut_qubo)
        fidelity = QNSPSA.get_fidelity(pre_qaoa.ansatz)
        optimizer = QNSPSA(fidelity, maxiter=MAX_ITER)

    qaoa = QAOA(optimizer=optimizer, quantum_instance=quantum_instance, reps=reps,
               initial_point=init_parameters)

    return qaoa

def run(max_cut_problem, qaoa):
    # Run quantum algorithm QAOA
    algorithm = MinimumEigenOptimizer(qaoa)
    result = algorithm.solve(max_cut_problem)
    optimal_parameters = qaoa.optimal_params

    return result, optimal_parameters.copy()

def objective(trial):
    optimizer_name = trial.suggest_categorical("optimizer", optimizers.keys())
    reps = trial.suggest_int('reps', REPS_MIN, REPS_MAX + 1)
    parameters = []
    for i in range(reps):
        parameters.append(trial.suggest_uniform(f'gamma_{i}', GAMMA_MIN, GAMMA_MAX))

    for i in range(reps):
        parameters.append(trial.suggest_uniform(f'beta_{i}', BETA_MIN, BETA_MAX))

    optimizer = optimizers[optimizer_name]

    qaoa = init_qaoa(optimizer, reps, parameters)

    result, optimal_parameters = run(max_cut_qubo, qaoa)
    mean, distribution = max_cut.analyse(result)

    trial.set_user_attr(key="best", value=[mean,result,optimal_parameters, optimizer_name])

    return mean

study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=N_TRIALS, callbacks=[save_best_trial])

[I 2021-08-09 11:02:23,264] A new study created in memory with name: no-name-29da8f9d-5e9d-4a1f-87cc-48207dec263c
capi_return is NULL
Call-back cb_calcfc_in__cobyla__user__routines failed.
[W 2021-08-10 12:05:41,982] Trial 0 failed because of the following error: KeyError(1999004074)
Traceback (most recent call last):
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/optuna/_optimize.py", line 216, in _run_trial
    value_or_values = func(trial)
  File "/tmp/ipykernel_3967832/1927191478.py", line 13, in objective
    qaoa = init_qaoa(optimizer, reps, parameters)
  File "/tmp/ipykernel_3967832/1512632364.py", line 11, in init_qaoa
    MinimumEigenOptimizer(pre_qaoa).solve(max_cut_qubo)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit_optimization/algorithms/minimum_eigen_optimizer.py", line 197, in solve
    return self._solve_internal(operator, offset, problem_, problem)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit_optimization/algorithms/minimum_eigen_optimizer.py", line 210, in _solve_internal
    eigen_result = self._min_eigen_solver.compute_minimum_eigenvalue(operator)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/algorithms/minimum_eigen_solvers/vqe.py", line 490, in compute_minimum_eigenvalue
    opt_params, opt_value, nfev = self.optimizer.optimize(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/algorithms/optimizers/scipy_optimizer.py", line 140, in optimize
    res = minimize(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/scipy/optimize/_minimize.py", line 615, in minimize
    return _minimize_cobyla(fun, x0, args, constraints, **options)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/scipy/optimize/cobyla.py", line 250, in _minimize_cobyla
    xopt, info = _cobyla.minimize(calcfc, m=m, x=np.copy(x0), rhobeg=rhobeg,
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/scipy/optimize/cobyla.py", line 242, in calcfc
    f = fun(x, *args)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/algorithms/minimum_eigen_solvers/vqe.py", line 562, in energy_evaluation
    sampled_expect_op = self._circuit_sampler.convert(expect_op, params=param_bindings)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/opflow/converters/circuit_sampler.py", line 218, in convert
    sampled_statefn_dicts = self.sample_circuits(circuit_sfns=circs, param_bindings=p_b)
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/opflow/converters/circuit_sampler.py", line 329, in sample_circuits
    results = self.quantum_instance.execute(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/utils/quantum_instance.py", line 661, in execute
    tmp_result = tmp_fitter.filter.apply(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py", line 149, in apply
    new_counts_list = parallel_map(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/tools/parallel.py", line 132, in parallel_map
    return [task(values[0], *task_args, **task_kwargs)]
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py", line 209, in _apply_correction
    new_counts = self.apply(
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py", line 182, in apply
    res = minimize(fun, x0, method='SLSQP',
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/scipy/optimize/_minimize.py", line 617, in minimize
    return _minimize_slsqp(fun, x0, args, jac, bounds,
  File "/home/hm-tlacherm/.local/lib/python3.8/site-packages/scipy/optimize/slsqp.py", line 477, in _minimize_slsqp
    message=exit_modes[int(mode)], success=(mode == 0))
KeyError: 1999004074

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_3967832/1956785657.py in <module>
      1 study = optuna.create_study(direction='minimize')
----> 2 study.optimize(objective, n_trials=N_TRIALS, callbacks=[save_best_trial])

~/.local/lib/python3.8/site-packages/optuna/study.py in optimize(self, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)
    399             )
    400 
--> 401         _optimize(
    402             study=self,
    403             func=func,

~/.local/lib/python3.8/site-packages/optuna/_optimize.py in _optimize(study, func, n_trials, timeout, n_jobs, catch, callbacks, gc_after_trial, show_progress_bar)
     63     try:
     64         if n_jobs == 1:
---> 65             _optimize_sequential(
     66                 study,
     67                 func,

~/.local/lib/python3.8/site-packages/optuna/_optimize.py in _optimize_sequential(study, func, n_trials, timeout, catch, callbacks, gc_after_trial, reseed_sampler_rng, time_start, progress_bar)
    160 
    161         try:
--> 162             trial = _run_trial(study, func, catch)
    163         except Exception:
    164             raise

~/.local/lib/python3.8/site-packages/optuna/_optimize.py in _run_trial(study, func, catch)
    265 
    266     if state == TrialState.FAIL and func_err is not None and not isinstance(func_err, catch):
--> 267         raise func_err
    268     return trial
    269 

~/.local/lib/python3.8/site-packages/optuna/_optimize.py in _run_trial(study, func, catch)
    214 
    215     try:
--> 216         value_or_values = func(trial)
    217     except exceptions.TrialPruned as e:
    218         # TODO(mamu): Handle multi-objective cases.

/tmp/ipykernel_3967832/1927191478.py in objective(trial)
     11     optimizer = optimizers[optimizer_name]
     12 
---> 13     qaoa = init_qaoa(optimizer, reps, parameters)
     14 
     15     result, optimal_parameters = run(max_cut_qubo, qaoa)

/tmp/ipykernel_3967832/1512632364.py in init_qaoa(optimizer, reps, init_parameters)
      9         # generate QAOA to get ansatz
     10         pre_qaoa = QAOA(optimizer=COBYLA(maxiter=1, tol=0), quantum_instance=quantum_instance, reps=reps, initial_point=init_parameters)
---> 11         MinimumEigenOptimizer(pre_qaoa).solve(max_cut_qubo)
     12         fidelity = QNSPSA.get_fidelity(pre_qaoa.ansatz)
     13         optimizer = QNSPSA(fidelity, maxiter=MAX_ITER)

~/.local/lib/python3.8/site-packages/qiskit_optimization/algorithms/minimum_eigen_optimizer.py in solve(self, problem)
    195         operator, offset = problem_.to_ising()
    196 
--> 197         return self._solve_internal(operator, offset, problem_, problem)
    198 
    199     def _solve_internal(

~/.local/lib/python3.8/site-packages/qiskit_optimization/algorithms/minimum_eigen_optimizer.py in _solve_internal(self, operator, offset, converted_problem, original_problem)
    208         if operator.num_qubits > 0:
    209             # approximate ground state of operator using min eigen solver
--> 210             eigen_result = self._min_eigen_solver.compute_minimum_eigenvalue(operator)
    211             # analyze results
    212             raw_samples = None

~/.local/lib/python3.8/site-packages/qiskit/algorithms/minimum_eigen_solvers/vqe.py in compute_minimum_eigenvalue(self, operator, aux_operators)
    488 
    489         start_time = time()
--> 490         opt_params, opt_value, nfev = self.optimizer.optimize(
    491             num_vars=len(initial_point),
    492             objective_function=energy_evaluation,

~/.local/lib/python3.8/site-packages/qiskit/algorithms/optimizers/scipy_optimizer.py in optimize(self, num_vars, objective_function, gradient_function, variable_bounds, initial_point)
    138         )
    139 
--> 140         res = minimize(
    141             fun=objective_function,
    142             x0=initial_point,

~/.local/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    613                              **options)
    614     elif meth == 'cobyla':
--> 615         return _minimize_cobyla(fun, x0, args, constraints, **options)
    616     elif meth == 'slsqp':
    617         return _minimize_slsqp(fun, x0, args, jac, bounds,

~/.local/lib/python3.8/site-packages/scipy/optimize/cobyla.py in _minimize_cobyla(fun, x0, args, constraints, rhobeg, tol, maxiter, disp, catol, **unknown_options)
    248 
    249     info = np.zeros(4, np.float64)
--> 250     xopt, info = _cobyla.minimize(calcfc, m=m, x=np.copy(x0), rhobeg=rhobeg,
    251                                   rhoend=rhoend, iprint=iprint, maxfun=maxfun,
    252                                   dinfo=info)

~/.local/lib/python3.8/site-packages/scipy/optimize/cobyla.py in calcfc(x, con)
    240 
    241     def calcfc(x, con):
--> 242         f = fun(x, *args)
    243         i = 0
    244         for size, c in izip(cons_lengths, constraints):

~/.local/lib/python3.8/site-packages/qiskit/algorithms/minimum_eigen_solvers/vqe.py in energy_evaluation(parameters)
    560 
    561             start_time = time()
--> 562             sampled_expect_op = self._circuit_sampler.convert(expect_op, params=param_bindings)
    563             means = np.real(sampled_expect_op.eval())
    564 

~/.local/lib/python3.8/site-packages/qiskit/opflow/converters/circuit_sampler.py in convert(self, operator, params)
    216         circs = list(self._circuit_ops_cache.values()) if not self._transpiled_circ_cache else None
    217         p_b = cast(List[Dict[Parameter, float]], param_bindings)
--> 218         sampled_statefn_dicts = self.sample_circuits(circuit_sfns=circs, param_bindings=p_b)
    219 
    220         def replace_circuits_with_dicts(operator, param_index=0):

~/.local/lib/python3.8/site-packages/qiskit/opflow/converters/circuit_sampler.py in sample_circuits(self, circuit_sfns, param_bindings)
    327             ready_circs = self._transpiled_circ_cache
    328 
--> 329         results = self.quantum_instance.execute(
    330             ready_circs, had_transpiled=self._transpile_before_bind
    331         )

~/.local/lib/python3.8/site-packages/qiskit/utils/quantum_instance.py in execute(self, circuits, had_transpiled)
    659                             )
    660                         )
--> 661                     tmp_result = tmp_fitter.filter.apply(
    662                         tmp_result, self._meas_error_mitigation_method
    663                     )

~/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py in apply(self, raw_data, method)
    147             new_result = deepcopy(raw_data)
    148 
--> 149             new_counts_list = parallel_map(
    150                 self._apply_correction,
    151                 [resultidx for resultidx, _ in enumerate(raw_data.results)],

~/.local/lib/python3.8/site-packages/qiskit/tools/parallel.py in parallel_map(task, values, task_args, task_kwargs, num_processes)
    130         return []
    131     if len(values) == 1:
--> 132         return [task(values[0], *task_args, **task_kwargs)]
    133 
    134     Publisher().publish("terra.parallel.start", len(values))

~/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py in _apply_correction(self, resultidx, raw_data, method)
    207     def _apply_correction(self, resultidx, raw_data, method):
    208         """Wrapper to call apply with a counts dictionary."""
--> 209         new_counts = self.apply(
    210             raw_data.get_counts(resultidx), method=method)
    211         return resultidx, new_counts

~/.local/lib/python3.8/site-packages/qiskit/ignis/mitigation/measurement/filters.py in apply(self, raw_data, method)
    180                 cons = ({'type': 'eq', 'fun': lambda x: nshots - sum(x)})
    181                 bnds = tuple((0, nshots) for x in x0)
--> 182                 res = minimize(fun, x0, method='SLSQP',
    183                                constraints=cons, bounds=bnds, tol=1e-6)
    184                 raw_data2[data_idx] = res.x

~/.local/lib/python3.8/site-packages/scipy/optimize/_minimize.py in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
    615         return _minimize_cobyla(fun, x0, args, constraints, **options)
    616     elif meth == 'slsqp':
--> 617         return _minimize_slsqp(fun, x0, args, jac, bounds,
    618                                constraints, callback=callback, **options)
    619     elif meth == 'trust-constr':

~/.local/lib/python3.8/site-packages/scipy/optimize/slsqp.py in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, callback, **unknown_options)
    475     return OptimizeResult(x=x, fun=fx, jac=g[:-1], nit=int(majiter),
    476                           nfev=feval[0], njev=geval[0], status=int(mode),
--> 477                           message=exit_modes[int(mode)], success=(mode == 0))
    478 
    479 

KeyError: 1999004074

print(study.best_trial)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
/tmp/ipykernel_3967832/2634521730.py in <module>
----> 1 print(study.best_trial)

~/.local/lib/python3.8/site-packages/optuna/study.py in best_trial(self)
     96             )
     97 
---> 98         return copy.deepcopy(self._storage.get_best_trial(self._study_id))
     99 
    100     @property

~/.local/lib/python3.8/site-packages/optuna/storages/_in_memory.py in get_best_trial(self, study_id)
    309             best_trial_id = self._studies[study_id].best_trial_id
    310             if best_trial_id is None:
--> 311                 raise ValueError("No trials are completed yet.")
    312             elif len(self._studies[study_id].directions) > 1:
    313                 raise ValueError(

ValueError: No trials are completed yet.

fig = optuna.visualization.plot_optimization_history(study)
fig.show()

[W 2021-08-10 12:05:45,534] Study instance does not contain trials.

mean, result, optimal_params, optimizer_name = study.user_attrs["best"]
print(f"Optimizer: {optimizer_name}")
print(f"Optimal Parameters: {optimal_params}")

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_3967832/355356642.py in <module>
----> 1 mean, result, optimal_params, optimizer_name = study.user_attrs["best"]
      2 print(f"Optimizer: {optimizer_name}")
      3 print(f"Optimal Parameters: {optimal_params}")

KeyError: 'best'

max_cut.draw(result)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_3967832/2691994859.py in <module>
----> 1 max_cut.draw(result)

NameError: name 'result' is not defined

mean, distribution = max_cut.analyse(result, print_output=True)
max_cut.plot_histogram(distribution, mean)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
/tmp/ipykernel_3967832/1339108312.py in <module>
----> 1 mean, distribution = max_cut.analyse(result, print_output=True)
      2 max_cut.plot_histogram(distribution, mean)

NameError: name 'result' is not defined

import qiskit.tools.jupyter
%qiskit_version_table

/home/hm-tlacherm/.local/lib/python3.8/site-packages/qiskit/aqua/__init__.py:86: DeprecationWarning:

The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>

Version Information

Qiskit Software	Version
qiskit-terra	0.18.1
qiskit-aer	0.8.2
qiskit-ignis	0.6.0
qiskit-ibmq-provider	0.16.0
qiskit-aqua	0.9.4
qiskit	0.29.0
qiskit-nature	0.1.5
qiskit-optimization	0.2.1
System information
Python	3.8.6 (default, Jan 22 2021, 11:41:28) [GCC 8.4.1 20200928 (Red Hat 8.4.1-1)]
OS	Linux
CPUs	192
Memory (Gb)	6046.015735626221
Tue Aug 10 12:05:49 2021 CEST